Adaptive optical imaging leads to a more accurate characterization of the functional properties of neurons.

With the ability to correct for the aberrations introduced by biological specimens, adaptive optics—a method originally developed for astronomical telescopes—has been applied to optical microscopy to recover diffraction-limited imaging performance deep within living tissue. In particular, this technology has been used to improve image quality and provide a more accurate characterization of both structure and function of neurons in a variety of living organisms. Among its many highlights, adaptive optical microscopy has made it possible to image large volumes with diffraction-limited resolution in zebrafish larval brains, to resolve dendritic spines over 600μm deep in the mouse brain, and to more accurately characterize the orientation tuning properties of thalamic boutons in the primary visual cortex of awake mice.

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these 'universal' statistics.

Electron crystallography is widespread in material science applications, but for biological samples its use has been restricted to a handful of examples where two-dimensional (2D) crystals or helical samples were studied either by electron diffraction and/or imaging. Electron crystallography in cryoEM, was developed in the mid-1970s and used to solve the structure of several membrane proteins and some soluble proteins. In 2013, a new method for cryoEM was unveiled and named Micro-crystal Electron Diffraction, or MicroED, which is essentially three-dimensional (3D) electron crystallography of microscopic crystals. This method uses truly 3D crystals, that are about a billion times smaller than those typically used for X-ray crystallography, for electron diffraction studies. There are several important differences and some similarities between electron crystallography of 2D crystals and MicroED. In this review, we describe the development of these techniques, their similarities and differences, and offer our opinion of future directions in both fields.

Aggregated tau protein is associated with over 20 neurological disorders, which include Alzheimer's disease. Previous work has shown that tau's sequence segments VQIINK and VQIVYK drive its aggregation, but inhibitors based on the structure of the VQIVYK segment only partially inhibit full-length tau aggregation and are ineffective at inhibiting seeding by full-length fibrils. Here we show that the VQIINK segment is the more powerful driver of tau aggregation. Two structures of this segment determined by the cryo-electron microscopy method micro-electron diffraction explain its dominant influence on tau aggregation. Of practical significance, the structures lead to the design of inhibitors that not only inhibit tau aggregation but also inhibit the ability of exogenous full-length tau fibrils to seed intracellular tau in HEK293 biosensor cells into amyloid. We also raise the possibility that the two VQIINK structures represent amyloid polymorphs of tau that may account for a subset of prion-like strains of tau.

The assembly of sequence-specific enhancer-binding transcription factors (TFs) at cis-regulatory elements in the genome has long been regarded as the fundamental mechanism driving cell type-specific gene expression. However, despite extensive biochemical, genetic, and genomic studies in the past three decades, our understanding of molecular mechanisms underlying enhancer-mediated gene regulation remains incomplete. Recent advances in imaging technologies now enable direct visualization of TF-driven regulatory events and transcriptional activities at the single-cell, single-molecule level. The ability to observe the remarkably dynamic behavior of individual TFs in live cells at high spatiotemporal resolution has begun to provide novel mechanistic insights and promises new advances in deciphering causal-functional relationships of TF targeting, genome organization, and gene activation. In this review, we review current transcription imaging techniques and summarize converging results from various lines of research that may instigate a revision of models to describe key features of eukaryotic gene regulation.

Mechanics plays a key role in the development of higher organisms. However, understanding this relationship is complicated by the difficulty of modeling the link between local forces generated at the subcellular level and deformations observed at the tissue and whole-embryo levels. Here we propose an approach first developed for lipid bilayers and cell membranes, in which force-generation by cytoskeletal elements enters a continuum mechanics formulation for the full system in the form of local changes in preferred curvature. This allows us to express and solve the system using only tissue strains. Locations of preferred curvature are simply related to products of gene expression. A solution, in that context, means relaxing the system’s mechanical energy to yield global morphogenetic predictions that accommodate a tendency toward the local preferred curvature, without a need to explicitly model force-generation mechanisms at the molecular level. Our computational framework, which we call SPHARM-MECH, extends a 3D spherical harmonics parameterization known as SPHARM to combine this level of abstraction with a sparse shape representation. The integration of these two principles allows computer simulations to be performed in three dimensions on highly complex shapes, gene expression patterns, and mechanical constraints. We demonstrate our approach by modeling mesoderm invagination in the fruit-fly embryo, where local forces generated by the acto-myosin meshwork in the region of the future mesoderm lead to formation of a ventral tissue fold. The process is accompanied by substantial changes in cell shape and long-range cell movements. Applying SPHARM-MECH to whole-embryo live imaging data acquired with light-sheet microscopy reveals significant correlation between calculated and observed tissue movements. Our analysis predicts the observed cell shape anisotropy on the ventral side of the embryo and suggests an active mechanical role of mesoderm invagination in supporting the onset of germ-band extension.

RESULTS: Here we report the development, structure, and validation of a new RFP-based GECI, K-GECO1, based on a circularly permutated RFP derived from the sea anemone Entacmaea quadricolor. We have characterized the performance of K-GECO1 in cultured HeLa cells, dissociated neurons, stem-cell-derived cardiomyocytes, organotypic brain slices, zebrafish spinal cord in vivo, and mouse brain in vivo.

CONCLUSION: K-GECO1 is the archetype of a new lineage of GECIs based on the RFP eqFP578 scaffold. It offers high sensitivity and fast kinetics, similar or better than those of current state-of-the-art indicators, with diminished lysosomal accumulation and minimal blue-light photoactivation. Further refinements of the K-GECO1 lineage could lead to further improved variants with overall performance that exceeds that of the most highly optimized red GECIs.

A series of classical studies in non-human primates has revealed the neuronal activity patterns underlying decision-making. However, the circuit mechanisms for such patterns remain largely unknown. Recent detailed circuit analyses in simpler neural systems have started to reveal the connectivity patterns underlying analogous processes. Here we review a few of these systems that share a particular connectivity pattern, namely mutual inhibition of lateral inhibition. Close examination of these systems suggests that this recurring connectivity pattern ('network motif') is a building block to enforce particular dynamics, which can be used not only for simple behavioral choice but also for more complex choices and other brain functions. Thus, a network motif provides an elementary computation that is not specific to a particular brain function and serves as an elementary building block in the brain.

Proceedings of the National Academy of Sciences of the United States of America. 2018 Jan 16:. doi: 10.1073/pnas.1716612115

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Multiple studies have investigated the mechanisms of aggressive behavior in Drosophila; however, little is known about the effects of chronic fighting experience. Here, we investigated if repeated fighting encounters would induce an internal state that could affect the expression of subsequent behavior. We trained wild-type males to become winners or losers by repeatedly pairing them with hypoaggressive or hyperaggressive opponents, respectively. As described previously, we observed that chronic losers tend to lose subsequent fights, while chronic winners tend to win them. Olfactory conditioning experiments showed that winning is perceived as rewarding, while losing is perceived as aversive. Moreover, the effect of chronic fighting experience generalized to other behaviors, such as gap-crossing and courtship. We propose that in response to repeatedly winning or losing aggressive encounters, male flies form an internal state that displays persistence and generalization; fight outcomes can also have positive or negative valence. Furthermore, we show that the activities of the PPL1-γ1pedc dopaminergic neuron and the MBON-γ1pedc>α/β mushroom body output neuron are required for aversion to an olfactory cue associated with losing fights.